// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H

namespace Eigen {

/** \class TensorPadding
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor padding class.
  * At the moment only padding with a constant value is supported.
  *
  */
namespace internal {
    template <typename PaddingDimensions, typename XprType> struct traits<TensorPaddingOp<PaddingDimensions, XprType>> : public traits<XprType>
    {
        typedef typename XprType::Scalar Scalar;
        typedef traits<XprType> XprTraits;
        typedef typename XprTraits::StorageKind StorageKind;
        typedef typename XprTraits::Index Index;
        typedef typename XprType::Nested Nested;
        typedef typename remove_reference<Nested>::type _Nested;
        static const int NumDimensions = XprTraits::NumDimensions;
        static const int Layout = XprTraits::Layout;
        typedef typename XprTraits::PointerType PointerType;
    };

    template <typename PaddingDimensions, typename XprType> struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense>
    {
        typedef const TensorPaddingOp<PaddingDimensions, XprType>& type;
    };

    template <typename PaddingDimensions, typename XprType>
    struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, typename eval<TensorPaddingOp<PaddingDimensions, XprType>>::type>
    {
        typedef TensorPaddingOp<PaddingDimensions, XprType> type;
    };

}  // end namespace internal

template <typename PaddingDimensions, typename XprType>
class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors>
{
public:
    typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested;
    typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind;
    typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims, const Scalar padding_value)
        : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value)
    {
    }

    EIGEN_DEVICE_FUNC
    const PaddingDimensions& padding() const { return m_padding_dims; }
    EIGEN_DEVICE_FUNC
    Scalar padding_value() const { return m_padding_value; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

protected:
    typename XprType::Nested m_xpr;
    const PaddingDimensions m_padding_dims;
    const Scalar m_padding_value;
};

// Eval as rvalue
template <typename PaddingDimensions, typename ArgType, typename Device> struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device>
{
    typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType;
    typedef typename XprType::Index Index;
    static const int NumDims = internal::array_size<PaddingDimensions>::value;
    typedef DSizes<Index, NumDims> Dimensions;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    enum
    {
        IsAligned = true,
        PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
        BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
        PreferBlockAccess = true,
        Layout = TensorEvaluator<ArgType, Device>::Layout,
        CoordAccess = true,
        RawAccess = false
    };

    typedef typename internal::remove_const<Scalar>::type ScalarNoConst;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
        : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()), m_device(device)
    {
        // The padding op doesn't change the rank of the tensor. Directly padding a scalar would lead
        // to a vector, which doesn't make sense. Instead one should reshape the scalar into a vector
        // of 1 element first and then pad.
        EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);

        // Compute dimensions
        m_dimensions = m_impl.dimensions();
        for (int i = 0; i < NumDims; ++i) { m_dimensions[i] += m_padding[i].first + m_padding[i].second; }
        const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            m_inputStrides[0] = 1;
            m_outputStrides[0] = 1;
            for (int i = 1; i < NumDims; ++i)
            {
                m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
                m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
            }
            m_outputStrides[NumDims] = m_outputStrides[NumDims - 1] * m_dimensions[NumDims - 1];
        }
        else
        {
            m_inputStrides[NumDims - 1] = 1;
            m_outputStrides[NumDims] = 1;
            for (int i = NumDims - 2; i >= 0; --i)
            {
                m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
                m_outputStrides[i + 1] = m_outputStrides[i + 2] * m_dimensions[i + 1];
            }
            m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0];
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
    {
        m_impl.evalSubExprsIfNeeded(NULL);
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done)
    {
        m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
        eigen_assert(index < dimensions().TotalSize());
        Index inputIndex = 0;
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            EIGEN_UNROLL_LOOP
            for (int i = NumDims - 1; i > 0; --i)
            {
                const Index idx = index / m_outputStrides[i];
                if (isPaddingAtIndexForDim(idx, i))
                {
                    return m_paddingValue;
                }
                inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
                index -= idx * m_outputStrides[i];
            }
            if (isPaddingAtIndexForDim(index, 0))
            {
                return m_paddingValue;
            }
            inputIndex += (index - m_padding[0].first);
        }
        else
        {
            EIGEN_UNROLL_LOOP
            for (int i = 0; i < NumDims - 1; ++i)
            {
                const Index idx = index / m_outputStrides[i + 1];
                if (isPaddingAtIndexForDim(idx, i))
                {
                    return m_paddingValue;
                }
                inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
                index -= idx * m_outputStrides[i + 1];
            }
            if (isPaddingAtIndexForDim(index, NumDims - 1))
            {
                return m_paddingValue;
            }
            inputIndex += (index - m_padding[NumDims - 1].first);
        }
        return m_impl.coeff(inputIndex);
    }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            return packetColMajor(index);
        }
        return packetRowMajor(index);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        TensorOpCost cost = m_impl.costPerCoeff(vectorized);
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            EIGEN_UNROLL_LOOP
            for (int i = 0; i < NumDims; ++i) updateCostPerDimension(cost, i, i == 0);
        }
        else
        {
            EIGEN_UNROLL_LOOP
            for (int i = NumDims - 1; i >= 0; --i) updateCostPerDimension(cost, i, i == NumDims - 1);
        }
        return cost;
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        const size_t target_size = m_device.lastLevelCacheSize();
        return internal::TensorBlockResourceRequirements::merge(internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
                                                                m_impl.getResourceRequirements());
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        // If one of the dimensions is zero, return empty block view.
        if (desc.size() == 0)
        {
            return TensorBlock(internal::TensorBlockKind::kView, NULL, desc.dimensions());
        }

        static const bool IsColMajor = Layout == static_cast<int>(ColMajor);
        const int inner_dim_idx = IsColMajor ? 0 : NumDims - 1;

        Index offset = desc.offset();

        // Compute offsets in the output tensor corresponding to the desc.offset().
        DSizes<Index, NumDims> output_offsets;
        for (int i = NumDims - 1; i > 0; --i)
        {
            const int dim = IsColMajor ? i : NumDims - i - 1;
            const int stride_dim = IsColMajor ? dim : dim + 1;
            output_offsets[dim] = offset / m_outputStrides[stride_dim];
            offset -= output_offsets[dim] * m_outputStrides[stride_dim];
        }
        output_offsets[inner_dim_idx] = offset;

        // Offsets in the input corresponding to output offsets.
        DSizes<Index, NumDims> input_offsets = output_offsets;
        for (int i = 0; i < NumDims; ++i)
        {
            const int dim = IsColMajor ? i : NumDims - i - 1;
            input_offsets[dim] = input_offsets[dim] - m_padding[dim].first;
        }

        // Compute offset in the input buffer (at this point it might be illegal and
        // point outside of the input buffer, because we don't check for negative
        // offsets, it will be autocorrected in the block iteration loop below).
        Index input_offset = 0;
        for (int i = 0; i < NumDims; ++i)
        {
            const int dim = IsColMajor ? i : NumDims - i - 1;
            input_offset += input_offsets[dim] * m_inputStrides[dim];
        }

        // Destination buffer and scratch buffer both indexed from 0 and have the
        // same dimensions as the requested block (for destination buffer this
        // property is guaranteed by `desc.destination()`).
        Index output_offset = 0;
        const DSizes<Index, NumDims> output_strides = internal::strides<Layout>(desc.dimensions());

        // NOTE(ezhulenev): We initialize bock iteration state for `NumDims - 1`
        // dimensions, skipping innermost dimension. In theory it should be possible
        // to squeeze matching innermost dimensions, however in practice that did
        // not show any improvements in benchmarks. Also in practice first outer
        // dimension usually has padding, and will prevent squeezing.

        // Initialize output block iterator state. Dimension in this array are
        // always in inner_most -> outer_most order (col major layout).
        array<BlockIteratorState, NumDims - 1> it;
        for (int i = 0; i < NumDims - 1; ++i)
        {
            const int dim = IsColMajor ? i + 1 : NumDims - i - 2;
            it[i].count = 0;
            it[i].size = desc.dimension(dim);

            it[i].input_stride = m_inputStrides[dim];
            it[i].input_span = it[i].input_stride * (it[i].size - 1);

            it[i].output_stride = output_strides[dim];
            it[i].output_span = it[i].output_stride * (it[i].size - 1);
        }

        const Index input_inner_dim_size = static_cast<Index>(m_impl.dimensions()[inner_dim_idx]);

        // Total output size.
        const Index output_size = desc.size();

        // We will fill inner dimension of this size in the output. It might be
        // larger than the inner dimension in the input, so we might have to pad
        // before/after we copy values from the input inner dimension.
        const Index output_inner_dim_size = desc.dimension(inner_dim_idx);

        // How many values to fill with padding BEFORE reading from the input inner
        // dimension.
        const Index output_inner_pad_before_size =
            input_offsets[inner_dim_idx] < 0 ? numext::mini(numext::abs(input_offsets[inner_dim_idx]), output_inner_dim_size) : 0;

        // How many values we can actually copy from the input inner dimension.
        const Index output_inner_copy_size = numext::mini(
            // Want to copy from input.
            (output_inner_dim_size - output_inner_pad_before_size),
            // Can copy from input.
            numext::maxi(input_inner_dim_size - (input_offsets[inner_dim_idx] + output_inner_pad_before_size), Index(0)));

        eigen_assert(output_inner_copy_size >= 0);

        // How many values to fill with padding AFTER reading from the input inner
        // dimension.
        const Index output_inner_pad_after_size = (output_inner_dim_size - output_inner_copy_size - output_inner_pad_before_size);

        // Sanity check, sum of all sizes must be equal to the output size.
        eigen_assert(output_inner_dim_size == (output_inner_pad_before_size + output_inner_copy_size + output_inner_pad_after_size));

        // Keep track of current coordinates and padding in the output.
        DSizes<Index, NumDims> output_coord = output_offsets;
        DSizes<Index, NumDims> output_padded;
        for (int i = 0; i < NumDims; ++i)
        {
            const int dim = IsColMajor ? i : NumDims - i - 1;
            output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
        }

        typedef internal::StridedLinearBufferCopy<ScalarNoConst, Index> LinCopy;

        // Prepare storage for the materialized padding result.
        const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);

        // TODO(ezhulenev): Squeeze multiple non-padded inner dimensions into a
        // single logical inner dimension.

        // When possible we squeeze writes for the innermost (only if non-padded)
        // dimension with the first padded dimension. This allows to reduce the
        // number of calls to LinCopy and better utilize vector instructions.
        const bool squeeze_writes = NumDims > 1 &&
                                    // inner dimension is not padded
                                    (input_inner_dim_size == m_dimensions[inner_dim_idx]) &&
                                    // and equal to the block inner dimension
                                    (input_inner_dim_size == output_inner_dim_size);

        const int squeeze_dim = IsColMajor ? inner_dim_idx + 1 : inner_dim_idx - 1;

        // Maximum coordinate on a squeeze dimension that we can write to.
        const Index squeeze_max_coord = squeeze_writes ? numext::mini(
                                                             // max non-padded element in the input
                                                             static_cast<Index>(m_dimensions[squeeze_dim] - m_padding[squeeze_dim].second),
                                                             // max element in the output buffer
                                                             static_cast<Index>(output_offsets[squeeze_dim] + desc.dimension(squeeze_dim))) :
                                                         static_cast<Index>(0);

        // Iterate copying data from `m_impl.data()` to the output buffer.
        for (Index size = 0; size < output_size;)
        {
            // Detect if we are in the padded region (exclude innermost dimension).
            bool is_padded = false;
            for (int j = 1; j < NumDims; ++j)
            {
                const int dim = IsColMajor ? j : NumDims - j - 1;
                is_padded = output_padded[dim];
                if (is_padded)
                    break;
            }

            if (is_padded)
            {
                // Fill single innermost dimension with padding value.
                size += output_inner_dim_size;

                LinCopy::template Run<LinCopy::Kind::FillLinear>(
                    typename LinCopy::Dst(output_offset, 1, block_storage.data()), typename LinCopy::Src(0, 0, &m_paddingValue), output_inner_dim_size);
            }
            else if (squeeze_writes)
            {
                // Squeeze multiple reads from innermost dimensions.
                const Index squeeze_num = squeeze_max_coord - output_coord[squeeze_dim];
                size += output_inner_dim_size * squeeze_num;

                // Copy `squeeze_num` inner dimensions from input to output.
                LinCopy::template Run<LinCopy::Kind::Linear>(typename LinCopy::Dst(output_offset, 1, block_storage.data()),
                                                             typename LinCopy::Src(input_offset, 1, m_impl.data()),
                                                             output_inner_dim_size * squeeze_num);

                // Update iteration state for only `squeeze_num - 1` processed inner
                // dimensions, because we have another iteration state update at the end
                // of the loop that will update iteration state for the last inner
                // processed dimension.
                it[0].count += (squeeze_num - 1);
                input_offset += it[0].input_stride * (squeeze_num - 1);
                output_offset += it[0].output_stride * (squeeze_num - 1);
                output_coord[squeeze_dim] += (squeeze_num - 1);
            }
            else
            {
                // Single read from innermost dimension.
                size += output_inner_dim_size;

                {  // Fill with padding before copying from input inner dimension.
                    const Index out = output_offset;

                    LinCopy::template Run<LinCopy::Kind::FillLinear>(
                        typename LinCopy::Dst(out, 1, block_storage.data()), typename LinCopy::Src(0, 0, &m_paddingValue), output_inner_pad_before_size);
                }

                {  // Copy data from input inner dimension.
                    const Index out = output_offset + output_inner_pad_before_size;
                    const Index in = input_offset + output_inner_pad_before_size;

                    eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);

                    LinCopy::template Run<LinCopy::Kind::Linear>(
                        typename LinCopy::Dst(out, 1, block_storage.data()), typename LinCopy::Src(in, 1, m_impl.data()), output_inner_copy_size);
                }

                {  // Fill with padding after copying from input inner dimension.
                    const Index out = output_offset + output_inner_pad_before_size + output_inner_copy_size;

                    LinCopy::template Run<LinCopy::Kind::FillLinear>(
                        typename LinCopy::Dst(out, 1, block_storage.data()), typename LinCopy::Src(0, 0, &m_paddingValue), output_inner_pad_after_size);
                }
            }

            for (int j = 0; j < NumDims - 1; ++j)
            {
                const int dim = IsColMajor ? j + 1 : NumDims - j - 2;

                if (++it[j].count < it[j].size)
                {
                    input_offset += it[j].input_stride;
                    output_offset += it[j].output_stride;
                    output_coord[dim] += 1;
                    output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
                    break;
                }
                it[j].count = 0;
                input_offset -= it[j].input_span;
                output_offset -= it[j].output_span;
                output_coord[dim] -= it[j].size - 1;
                output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
            }
        }

        return block_storage.AsTensorMaterializedBlock();
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_impl.bind(cgh); }
#endif

private:
    struct BlockIteratorState
    {
        BlockIteratorState() : count(0), size(0), input_stride(0), input_span(0), output_stride(0), output_span(0) {}

        Index count;
        Index size;
        Index input_stride;
        Index input_span;
        Index output_stride;
        Index output_span;
    };

    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isPaddingAtIndexForDim(Index index, int dim_index) const
    {
#if defined(EIGEN_HAS_INDEX_LIST)
        return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) && index < m_padding[dim_index].first) ||
               (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) && index >= m_dimensions[dim_index] - m_padding[dim_index].second);
#else
        return (index < m_padding[dim_index].first) || (index >= m_dimensions[dim_index] - m_padding[dim_index].second);
#endif
    }

    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isLeftPaddingCompileTimeZero(int dim_index) const
    {
#if defined(EIGEN_HAS_INDEX_LIST)
        return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0);
#else
        EIGEN_UNUSED_VARIABLE(dim_index);
        return false;
#endif
    }

    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isRightPaddingCompileTimeZero(int dim_index) const
    {
#if defined(EIGEN_HAS_INDEX_LIST)
        return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0);
#else
        EIGEN_UNUSED_VARIABLE(dim_index);
        return false;
#endif
    }

    void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const
    {
        const double in = static_cast<double>(m_impl.dimensions()[i]);
        const double out = in + m_padding[i].first + m_padding[i].second;
        if (out == 0)
            return;
        const double reduction = in / out;
        cost *= reduction;
        if (first)
        {
            cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() + reduction * (1 * TensorOpCost::AddCost<Index>()));
        }
        else
        {
            cost += TensorOpCost(0,
                                 0,
                                 2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
                                     reduction * (2 * TensorOpCost::MulCost<Index>() + 1 * TensorOpCost::DivCost<Index>()));
        }
    }

protected:
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
    {
        EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
        eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());

        const Index initialIndex = index;
        Index inputIndex = 0;
        EIGEN_UNROLL_LOOP
        for (int i = NumDims - 1; i > 0; --i)
        {
            const Index firstIdx = index;
            const Index lastIdx = index + PacketSize - 1;
            const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
            const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
            const Index lastPaddedRight = m_outputStrides[i + 1];

            if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft)
            {
                // all the coefficient are in the padding zone.
                return internal::pset1<PacketReturnType>(m_paddingValue);
            }
            else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight)
            {
                // all the coefficient are in the padding zone.
                return internal::pset1<PacketReturnType>(m_paddingValue);
            }
            else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight))
            {
                // all the coefficient are between the 2 padding zones.
                const Index idx = index / m_outputStrides[i];
                inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
                index -= idx * m_outputStrides[i];
            }
            else
            {
                // Every other case
                return packetWithPossibleZero(initialIndex);
            }
        }

        const Index lastIdx = index + PacketSize - 1;
        const Index firstIdx = index;
        const Index lastPaddedLeft = m_padding[0].first;
        const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
        const Index lastPaddedRight = m_outputStrides[1];

        if (!isLeftPaddingCompileTimeZero(0) && lastIdx < lastPaddedLeft)
        {
            // all the coefficient are in the padding zone.
            return internal::pset1<PacketReturnType>(m_paddingValue);
        }
        else if (!isRightPaddingCompileTimeZero(0) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight)
        {
            // all the coefficient are in the padding zone.
            return internal::pset1<PacketReturnType>(m_paddingValue);
        }
        else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight))
        {
            // all the coefficient are between the 2 padding zones.
            inputIndex += (index - m_padding[0].first);
            return m_impl.template packet<Unaligned>(inputIndex);
        }
        // Every other case
        return packetWithPossibleZero(initialIndex);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
    {
        EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
        eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());

        const Index initialIndex = index;
        Index inputIndex = 0;
        EIGEN_UNROLL_LOOP
        for (int i = 0; i < NumDims - 1; ++i)
        {
            const Index firstIdx = index;
            const Index lastIdx = index + PacketSize - 1;
            const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i + 1];
            const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i + 1];
            const Index lastPaddedRight = m_outputStrides[i];

            if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft)
            {
                // all the coefficient are in the padding zone.
                return internal::pset1<PacketReturnType>(m_paddingValue);
            }
            else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight)
            {
                // all the coefficient are in the padding zone.
                return internal::pset1<PacketReturnType>(m_paddingValue);
            }
            else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight))
            {
                // all the coefficient are between the 2 padding zones.
                const Index idx = index / m_outputStrides[i + 1];
                inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
                index -= idx * m_outputStrides[i + 1];
            }
            else
            {
                // Every other case
                return packetWithPossibleZero(initialIndex);
            }
        }

        const Index lastIdx = index + PacketSize - 1;
        const Index firstIdx = index;
        const Index lastPaddedLeft = m_padding[NumDims - 1].first;
        const Index firstPaddedRight = (m_dimensions[NumDims - 1] - m_padding[NumDims - 1].second);
        const Index lastPaddedRight = m_outputStrides[NumDims - 1];

        if (!isLeftPaddingCompileTimeZero(NumDims - 1) && lastIdx < lastPaddedLeft)
        {
            // all the coefficient are in the padding zone.
            return internal::pset1<PacketReturnType>(m_paddingValue);
        }
        else if (!isRightPaddingCompileTimeZero(NumDims - 1) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight)
        {
            // all the coefficient are in the padding zone.
            return internal::pset1<PacketReturnType>(m_paddingValue);
        }
        else if ((isLeftPaddingCompileTimeZero(NumDims - 1) && isRightPaddingCompileTimeZero(NumDims - 1)) ||
                 (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight))
        {
            // all the coefficient are between the 2 padding zones.
            inputIndex += (index - m_padding[NumDims - 1].first);
            return m_impl.template packet<Unaligned>(inputIndex);
        }
        // Every other case
        return packetWithPossibleZero(initialIndex);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
    {
        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
        EIGEN_UNROLL_LOOP
        for (int i = 0; i < PacketSize; ++i) { values[i] = coeff(index + i); }
        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
        return rslt;
    }

    Dimensions m_dimensions;
    array<Index, NumDims + 1> m_outputStrides;
    array<Index, NumDims> m_inputStrides;
    TensorEvaluator<ArgType, Device> m_impl;
    PaddingDimensions m_padding;

    Scalar m_paddingValue;

    const Device EIGEN_DEVICE_REF m_device;
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
